為幫助使用者找到其可能喜好的物品，推薦系統普遍利用了資料分析的技巧，而實際生活中的推薦系統可應用於推薦電影、書籍、及音樂光碟等。當推薦系統之研究正成為一個重要且獨立的課題時，評價的機制在相關研究工作中扮演了重要的角色，在眾多的推薦技術當中，協同式過濾是最廣受認同的方法，其能正確地估計使用者對諸多未曾看過物品的評價，並將具有最高估計評價的物品推薦給使用者。在本論文中，我們將推薦系統中單一條件評價的觀念推廣成多個評價條件，亦即每一物品可從多方面進行評價，舉例來說，一間餐廳的好壞可由食物的美味、裝潢的好壞、及服務的優劣等分別評價。然而這些條件間往往是互相衝突的，因此我們認為此一多評價條件之推薦問題不能再被視為一最佳化問題，相對地我們在本論文中提出使用資料查詢的技術來解決此一多評價條件之推薦問題，經過實證研究後可發現我們的方法同時深具理論與實務價值。

Recommendation systems utilize data analysis techniques to the problem of helping users find the items they would like. Example applications include the recommendation systems for movies, books, CDs and many others. As recommendation systems emerge as an independent research area, the rating structure plays a critical role in recent studies. Among many alternatives, the collaborative filtering algorithms are generally accepted to be successful to estimate user ratings of unseen items and then to derive proper recommendations. In this thesis, we extend the concept of single-criterion ratings to multi-criteria ones, i.e., an item can be evaluated in many different aspects. For example, the goodness of a restaurant can be evaluated in terms of its food, decor, service and cost. Since there are usually conflicts among different criteria, the recommendation problem cannot be formulated as an optimization problem anymore. Instead, we propose in this thesis to use data query techniques to solve this multi-criteria recommendation problem. Empirical studies show that our approach is of both theoretical and practical values.

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